Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "171" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 57 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 55 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459872 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 1.884049 | 4.912056 | 12.365147 | 1.845614 | 3.484387 | 8.313352 | -0.162875 | -0.207770 | 0.6436 | 0.5984 | 0.3946 | nan | nan |
| 2459871 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 1.442559 | 3.732227 | 12.113700 | 1.293007 | 1.950088 | 6.692150 | -0.228472 | -0.157914 | 0.6473 | 0.5934 | 0.3954 | nan | nan |
| 2459870 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 3.207992 | 6.824728 | 8.863296 | 0.153703 | 1.680255 | 2.430574 | -0.210716 | -0.455092 | 0.6515 | 0.5994 | 0.3973 | nan | nan |
| 2459869 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 2.657319 | 4.703965 | 8.637600 | -0.051622 | 2.296967 | 4.122188 | 0.853848 | 0.000073 | 0.6661 | 0.6281 | 0.3873 | nan | nan |
| 2459868 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 3.025237 | 6.695055 | 13.259288 | 1.374835 | 0.804435 | 3.966367 | 0.132027 | 0.238495 | 0.6391 | 0.5930 | 0.4039 | nan | nan |
| 2459867 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 1.781389 | 4.539614 | 10.918191 | 0.795694 | 0.361868 | 1.445325 | 0.071505 | -0.300717 | 0.6507 | 0.5971 | 0.4075 | nan | nan |
| 2459866 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 2.108443 | 5.228848 | 6.648317 | 0.836164 | 0.031894 | 0.916199 | -0.491115 | -0.106036 | 0.6547 | 0.5986 | 0.4004 | nan | nan |
| 2459865 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 3.816222 | 8.846026 | 13.330809 | 0.074793 | 3.046104 | 7.519515 | 3.361318 | 1.802151 | 0.6768 | 0.6083 | 0.3822 | nan | nan |
| 2459864 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 2.823867 | 9.087409 | 5.769161 | -0.992969 | 0.395646 | 4.038380 | 0.985973 | 1.243318 | 0.6523 | 0.5786 | 0.4233 | nan | nan |
| 2459863 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.612971 | 4.869763 | -1.132752 | -0.851700 | -0.495423 | -0.707105 | -0.494537 | 0.072719 | 0.6461 | 0.5677 | 0.4158 | nan | nan |
| 2459862 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 1.237445 | 5.381482 | 9.955387 | 0.819685 | 0.658258 | 6.124185 | -0.210676 | -0.413593 | 0.6301 | 0.5993 | 0.4200 | nan | nan |
| 2459861 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.017515 | 2.907672 | -2.166507 | -0.445803 | -1.194739 | -2.142464 | -0.543855 | -0.507064 | 0.6624 | 0.5758 | 0.4291 | nan | nan |
| 2459860 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.427463 | 3.288814 | 7.626267 | 0.058157 | 1.262290 | 4.523127 | -0.171642 | -0.564387 | 0.6728 | 0.5888 | 0.4262 | nan | nan |
| 2459859 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.254313 | 2.473893 | -2.576792 | 0.366808 | -0.830720 | -1.802992 | -0.309750 | -0.098712 | 0.6773 | 0.5916 | 0.4247 | nan | nan |
| 2459858 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -0.322313 | 2.570858 | -2.728890 | 0.213363 | -0.914839 | -1.171654 | -0.399993 | -0.236156 | 0.6873 | 0.5973 | 0.4363 | 2.554990 | 2.081126 |
| 2459857 | not_connected | 0.00% | 100.00% | 100.00% | 0.00% | - | - | 1.822536 | 3.373211 | 1.520690 | 1.331699 | -0.430601 | 0.083682 | -1.504822 | -2.130699 | 0.0292 | 0.0293 | 0.0006 | nan | nan |
| 2459856 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 0.893962 | 5.024504 | 7.146122 | -0.148006 | -0.309535 | 1.642503 | -0.607468 | -0.371254 | 0.6804 | 0.6161 | 0.4219 | 2.763073 | 2.195293 |
| 2459855 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.651843 | 5.539403 | 7.928781 | 0.385077 | -0.521077 | 1.326300 | -0.660695 | -0.579060 | 0.6517 | 0.6295 | 0.4432 | 2.749630 | 2.150417 |
| 2459854 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.361715 | 5.907096 | 5.728786 | -0.565681 | -0.663239 | 1.700806 | -0.627484 | -0.637082 | 0.6770 | 0.6360 | 0.4606 | 2.799796 | 2.118599 |
| 2459853 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 0.818240 | 5.441394 | 9.358166 | -0.049308 | -0.064410 | 1.785154 | -0.364690 | -0.317444 | 0.7051 | 0.5908 | 0.4530 | 3.352257 | 2.266019 |
| 2459852 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 3.002164 | 6.621582 | 9.256279 | 0.134002 | 4.078278 | 3.987862 | 7.074828 | 2.327156 | 0.7997 | 0.7750 | 0.2649 | 3.955265 | 3.532607 |
| 2459851 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
| 2459850 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 0.817908 | 6.878561 | 8.879559 | -0.043857 | 0.828945 | 5.875766 | -0.200465 | 3.071102 | 0.7086 | 0.6707 | 0.3702 | 2.803273 | 2.092635 |
| 2459849 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.042386 | 6.070842 | 18.169478 | 0.526725 | 0.515386 | 2.644560 | -0.222885 | 0.331244 | 0.7054 | 0.6600 | 0.3877 | 3.569105 | 2.762000 |
| 2459848 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.254984 | 4.412662 | 11.036468 | 0.562507 | 1.436585 | 6.080866 | -0.204221 | -0.394177 | 0.6829 | 0.6756 | 0.3948 | 2.794286 | 2.288513 |
| 2459847 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.339975 | 5.012668 | 10.452231 | 0.904618 | 0.518877 | 9.736389 | -0.517150 | -0.508381 | 0.6863 | 0.5957 | 0.4496 | 3.338619 | 2.427621 |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 171 | N16 | not_connected | ee Power | 12.365147 | 4.912056 | 1.884049 | 1.845614 | 12.365147 | 8.313352 | 3.484387 | -0.207770 | -0.162875 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 171 | N16 | not_connected | ee Power | 12.113700 | 3.732227 | 1.442559 | 1.293007 | 12.113700 | 6.692150 | 1.950088 | -0.157914 | -0.228472 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 171 | N16 | not_connected | ee Power | 8.863296 | 3.207992 | 6.824728 | 8.863296 | 0.153703 | 1.680255 | 2.430574 | -0.210716 | -0.455092 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 171 | N16 | not_connected | ee Power | 8.637600 | 2.657319 | 4.703965 | 8.637600 | -0.051622 | 2.296967 | 4.122188 | 0.853848 | 0.000073 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 171 | N16 | not_connected | ee Power | 13.259288 | 3.025237 | 6.695055 | 13.259288 | 1.374835 | 0.804435 | 3.966367 | 0.132027 | 0.238495 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 171 | N16 | not_connected | ee Power | 10.918191 | 1.781389 | 4.539614 | 10.918191 | 0.795694 | 0.361868 | 1.445325 | 0.071505 | -0.300717 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 171 | N16 | not_connected | ee Power | 6.648317 | 5.228848 | 2.108443 | 0.836164 | 6.648317 | 0.916199 | 0.031894 | -0.106036 | -0.491115 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 171 | N16 | not_connected | ee Power | 13.330809 | 3.816222 | 8.846026 | 13.330809 | 0.074793 | 3.046104 | 7.519515 | 3.361318 | 1.802151 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 171 | N16 | not_connected | nn Shape | 9.087409 | 9.087409 | 2.823867 | -0.992969 | 5.769161 | 4.038380 | 0.395646 | 1.243318 | 0.985973 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 171 | N16 | not_connected | nn Shape | 4.869763 | 0.612971 | 4.869763 | -1.132752 | -0.851700 | -0.495423 | -0.707105 | -0.494537 | 0.072719 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 171 | N16 | not_connected | ee Power | 9.955387 | 1.237445 | 5.381482 | 9.955387 | 0.819685 | 0.658258 | 6.124185 | -0.210676 | -0.413593 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 171 | N16 | not_connected | nn Shape | 2.907672 | 2.907672 | -0.017515 | -0.445803 | -2.166507 | -2.142464 | -1.194739 | -0.507064 | -0.543855 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 171 | N16 | not_connected | ee Power | 7.626267 | 0.427463 | 3.288814 | 7.626267 | 0.058157 | 1.262290 | 4.523127 | -0.171642 | -0.564387 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 171 | N16 | not_connected | nn Shape | 2.473893 | -0.254313 | 2.473893 | -2.576792 | 0.366808 | -0.830720 | -1.802992 | -0.309750 | -0.098712 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 171 | N16 | not_connected | nn Shape | 2.570858 | 2.570858 | -0.322313 | 0.213363 | -2.728890 | -1.171654 | -0.914839 | -0.236156 | -0.399993 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 171 | N16 | not_connected | nn Shape | 3.373211 | 3.373211 | 1.822536 | 1.331699 | 1.520690 | 0.083682 | -0.430601 | -2.130699 | -1.504822 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 171 | N16 | not_connected | ee Power | 7.146122 | 0.893962 | 5.024504 | 7.146122 | -0.148006 | -0.309535 | 1.642503 | -0.607468 | -0.371254 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 171 | N16 | not_connected | ee Power | 7.928781 | 5.539403 | 1.651843 | 0.385077 | 7.928781 | 1.326300 | -0.521077 | -0.579060 | -0.660695 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 171 | N16 | not_connected | nn Shape | 5.907096 | 5.907096 | 1.361715 | -0.565681 | 5.728786 | 1.700806 | -0.663239 | -0.637082 | -0.627484 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 171 | N16 | not_connected | ee Power | 9.358166 | 5.441394 | 0.818240 | -0.049308 | 9.358166 | 1.785154 | -0.064410 | -0.317444 | -0.364690 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 171 | N16 | not_connected | ee Power | 9.256279 | 3.002164 | 6.621582 | 9.256279 | 0.134002 | 4.078278 | 3.987862 | 7.074828 | 2.327156 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 171 | N16 | not_connected | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 171 | N16 | not_connected | ee Power | 8.879559 | 0.817908 | 6.878561 | 8.879559 | -0.043857 | 0.828945 | 5.875766 | -0.200465 | 3.071102 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 171 | N16 | not_connected | ee Power | 18.169478 | 1.042386 | 6.070842 | 18.169478 | 0.526725 | 0.515386 | 2.644560 | -0.222885 | 0.331244 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 171 | N16 | not_connected | ee Power | 11.036468 | 4.412662 | 1.254984 | 0.562507 | 11.036468 | 6.080866 | 1.436585 | -0.394177 | -0.204221 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 171 | N16 | not_connected | ee Power | 10.452231 | 5.012668 | 1.339975 | 0.904618 | 10.452231 | 9.736389 | 0.518877 | -0.508381 | -0.517150 |